A Bayesian model for joint segmentation and registration

Neuroimage. 2006 May 15;31(1):228-39. doi: 10.1016/j.neuroimage.2005.11.044. Epub 2006 Feb 7.

Abstract

A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Artifacts
  • Bayes Theorem*
  • Brain / pathology*
  • Brain Diseases / diagnosis*
  • Brain Mapping
  • Cerebral Ventricles / pathology
  • Dominance, Cerebral / physiology
  • Humans
  • Image Enhancement*
  • Image Processing, Computer-Assisted / statistics & numerical data*
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Models, Statistical
  • Thalamus / pathology